Our aim is to ease the deployment of deep neural networks on energy-efficient FPGA devices, which will enable portable surveillance applications to harness the power of deep learning.

What is the unmet need in society that your idea will fulfill ?

Security agencies deploy surveillance equipment for monitoring such as cameras and drones. Recently deep learning algorithms have proven their worth for image classification and object detection which are essential to surveillance.Our project will provide the portable hardware for such applications.

Who needs it ? How many would benefit ?

Though the possibilities are endless but security agencies are our main focus who need to deploy advanced machine learning algorithms on drones and battery-powered camera systems. We will provide a very efficient hardware for their needs which can run complex algorithms at less than 5W power.

How will the solution works

We will develop upon state-of-the-art libraries (FINN framework) provided by Xilinx Corporation to implement a variety of sample deep neural networks on FPGAs. Our solution trains the deep network in such a way that it is suitable for FPGAs. Then the deployment of the trained weights and network architecture is done on the FPGA. Our hardware runs as less than 5 watts of power and performs comparable to 250W GPU devices. Thus enabling a battery powered deep learning processor.

Who are your competitors ? How is your solution different

To the best of our knowledge, no one in Pakistan is focusing on FPGAs for security purposes except R&D sector of NESCOM. We will use an opensource library and latest state-of-the-art framework to deploy neural networks on FPGAs which has not yet been achieved here in Pakistan.